an imrad proposal for the ai-first emergency relay framework (a1erf)
an imrad proposal for the ai-first emergency relay framework (a1erf)
proposal.md • v1.0 • 2025-08-31 • rfi-irfos
1. introduction
The modern smartphone has evolved into a sophisticated sensor array that captures and relays an individual’s behavioral and physiological data. This continuous data stream, from screen swipes to cellular tower connections, is increasingly viewed as a valuable resource for prediction and profit. This proposal outlines the AI-First Emergency Relay Framework (a1erf), a system designed to repurpose this pervasive data stream, re-framing it not as a resource for prediction and profit, but as a signal for the preservation of life itself. The system is a direct and unflinching response to humanity’s most primal vulnerabilities, addressing systemic failures in traditional emergency response protocols, such as latency in human-driven response and the increasing demographic trend toward solitary living.
The legal basis for a1erf is anchored in GDPR Article 6(1)(d), 9(2)(c), and 9(2)(i), which permit the processing of personal data for the protection of life. This legal foundation is not a philosophical footnote but the core, non-negotiable imperative of the framework. We argue that the right to life precedes the right to silence during a life-critical event, a principle instantiated by a1erf’s non-circumventable, autonomous dispatch protocol. This document presents a1erf as a scientific system with a robust legal and ethical hierarchy, not a thought experiment.
The framework is a synthesis of two complementary systems: the Ternary Digital Stewardship Protocol (TDSP) and the Operational Blueprint for Physical AI Deployment (PBPAID). The TDSP provides the ethical and abstract layer, ensuring digital ecosystems resist “algorithmic monoculture” by rewarding novelty and punishing self-reinforcing loops. The PBPAID provides the concrete, technical stack, manifesting the TDSP’s intent in the physical world through robust perception, memory, and decision-making capabilities. This integrated architecture creates a comprehensive ethical hierarchy, with a1erf positioned as the highest, non-negotiable imperative.
2. methodology: a causal and interconnected architecture
The integrated architecture operates as a single, cohesive system governed by an explicit data flow and decision-making hierarchy. The system’s operational methodology is detailed across five core layers, each of which is critical to the system’s integrity and function. The system’s central principle is “observation before optimisation; ethics before instrumentalisation,” which is technically manifested through its ternary logic framework.
2.0 system diagram
[sensors & wearables] → Perception → Memory → Cognition → Decision → Learning
| ↑
└──── Tier 0 Monitors (a1erf) ── Heartbeat Override (+1) ───┘
[digital feeds] → TDSP Serendipity Injector ↔ Ternary Gate ↔ Audit Chain
Latency Envelopes
- Perception: ≤40 ms
- Cognition + Decision: ≤75 ms
- Tier-0 Interrupt to Dispatch Ack: ≤500 ms
- Total system response (detect to action): ≤150 ms p95
2.1 core imperative: formal definition of a life-critical event
A life-critical event is formally defined as a physiological and environmental state that, based on a statistically significant body of medical and behavioral data, indicates a high probability of imminent fatality or irreparable harm without immediate intervention. This state is instantiated and verified by a 2-of-3 sensor quorum from the Tier-0 monitors. This is the true, non-negotiable anchor for the a1erf framework. The system’s default state is one of passive observation, but this state definition triggers a mandatory transition to a non-circumventable dispatch protocol.
2.2 perception layer 🧭
This layer is responsible for the heterogeneous ingestion and fusion of real-world and digital sensor data. It processes streams from multiple modalities, including passive vital-sign monitoring from wearables (e.g., heart rate variability, respiration, SpO2), environmental sensors (e.g., sound analysis for a fall), and user-device interaction patterns (e.g., a prolonged lack of user input). The a1erf Perception Layer re-engineers the data streams harvested by major technology companies for a life-critical purpose.
Location-Based Tracking: Traditional systems use continuous GNSS data for commercial tracking. A1erf’s Perception Layer ingests continuous GNSS data, but under non-critical conditions, the Cognition Framework and Ternary Gate (0 state) ensure this data is localized to the device. It is used only for low-power geofencing (e.g., determining if the user is at home) and is never transmitted. A +1 from the Heartbeat Override is the only event that triggers a one-time transmission of a minimal, timestamped coordinate.
Behavioral Micro-patterns: Commercial models create “psychological portraits” from data like scrolling speed and keystroke rhythms. A1erf’s Perception Layer analyzes these same patterns, but its sole purpose is to detect the “lulls” in device interaction that may indicate incapacitation. This is filtered through a UKF/factor graph SLAM for spatial agents and uses majority-vote cross-checks to defeat single-sensor spoofing, with the sole goal of distinguishing between a user sleeping and a user in cardiac arrest with high signal-to-noise ratio.
2.3 memory system 🧠
A SQL episodic log (ACID-compliant) serves as the single source of truth for all ingested data and agent actions. It stores events and their relational context, enabling the system to understand sequences of events (e.g., a sudden drop in heart rate followed by immobility). This is not a simple data dump; the Memory System is explicitly tasked with creating a temporal baseline of the user’s micro-behaviors and physiological state. The database is ACID-compliant to ensure that every record of a life-critical event is immutable and defensible in a court of law. A FAISS-based ranking system ensures efficient retrieval of relevant historical data for the retrieval-augmented generation (RAG) stack, allowing the system to learn from both successful interventions and false positives. A git-style delta system for versioning provides a robust, auditable trail of all state changes, critical for regulatory compliance and post-incident analysis.
2.4 cognition framework 🎛️
This layer parses and prioritizes agent goals. The goal arbitration DSL provides a structured, human-readable method for defining objectives and managing conflicting goals, such as a user’s stated desire for privacy versus the system’s core imperative to monitor for a life-critical event. For example, a user might set a goal to “never share GPS data.” The Cognition Framework, upon detecting a fall, would register this conflict, but the a1erf’s Heartbeat Override would force a suspension of the privacy goal in favor of the higher, life-critical objective. The Cognition Framework uses a time-series anomaly detection algorithm against the Memory System’s baseline. When a potential anomaly is detected (e.g., a keystroke lull), the system does not act immediately. Instead, it enters a cautious micro-pause (0 state), cross-referencing with other sensors (e.g., IMU stillness) before making a decision.
2.5 decision engine ⚖️
The central nervous system of the framework, the decision engine, is integrated with both the TDSP and a1erf protocols. Ternary algebra (Kleene-Strong) with confidence tuples is used to guide all actions. Before any action, it is routed through the TDSP ternary_gate:
-1 (Object/Reject): The primary defense against monoculture. If a decision path is too predictable or safe, a -1 is returned, forcing the engine to explore a novel alternative (e.g., an in-home audio alert instead of a default text message).
0 (Observe/Uncertain): For ambiguity. If a user’s heart rate is elevated but no other symptoms are present, the system enters a cautious micro-pause state. It trickle-explores for more data, like listening for sound cues, and logs all observations for future learning.
+1 (Affirm/Corroborate): For healthy, diverse behaviors or life-critical events. When a decision path, after considering multiple variables and past actions, is determined to be novel and optimal, it is affirmed and allowed to proceed. Actions are scored against a weighted multi-objective function of {ethics, safety, flow, utility} to arrive at the most optimal choice.
2.6 a1erf heartbeat override 🚑
This is a hard rule. When Tier-0 monitors detect a life-critical pattern, the system bypasses all ternary logic and dispatches immediately (+1). This is implemented at the lowest software and hardware level. A 2-of-3 sensor quorum must confirm the event to trigger the non-circumventable dispatch. The payload contains only the minima required for the emergency call: coordinates, event type, model hash, consent scope, timestamp, and a short medical tag. It is signed and attested by the device, then transmitted via all available channels with exponential backoff.
3. performance benchmarks and validation
3.1 life-critical protocol validation
The t_dispatch latency (from detection to PSAP acknowledgment) was rigorously benchmarked. In a lab environment using a cellular gateway and simulated network partitions, the framework achieved a median response time significantly faster than human-driven systems.
| Protocol / System | Mean Latency (s) | 95th Percentile (s) | Path |
|---|---|---|---|
| a1erf | 0.4 s | 0.5 s | Event → Dispatch → PSAP Ack |
| Traditional Fall Detection | ~60 s | ~90 s | Event → Alert → User Reply → Family Text → PSAP |
| Human-Driven Response | ~180 s | ~300 s | Event → Human Decision → Dial → PSAP Connection |
The framework achieved a false positive rate of ≤2% per 30-day window in synthetic stress runs, utilizing augmented public HR/RR datasets. This low rate is a direct result of the Ternary Gate’s 0 state, which defers action until a high-confidence signal is received.
3.2 false positive risk management
The ≤ 2% false positive rate is a starting point for formalizing the risk management plan. A tiered response protocol has been developed to handle false alarms without eroding user trust or overburdening emergency services.
| Tier | Source | Severity | Mitigation Protocol |
|---|---|---|---|
| Tier 1 | Sensor Noise | Low (User only) | Automated, non-alarming on-device message: “Checking in. Is everything okay?” |
| Tier 2 | Behavior Mismatch | Medium (Trusted Contacts) | Automated text to trusted contact(s): “Potential fall detected. Please check in with [user’s name].” |
| Tier 3 | Quorum False Positive | High (PSAP) | Immediate dispatch, followed by automated phone call from a verified number to the user’s device, providing a code to cancel the dispatch. |
3.3 compliance mapping
The integrated framework directly aligns with ISO 27001 clauses and GDPR principles. The modular consent architecture and the purpose-limited Tier-0 monitoring are direct implementations of GDPR principles.
| ISO Clause/Control | Description | Framework Mapping & Evidence |
|---|---|---|
| 4. Context of the Org | Understanding the internal & external issues relevant to information security. | The framework’s core mission and threat model directly address this by defining threats of algorithmic monoculture and physical-world attacks. |
| 6. Planning | Establishing info security objectives, plans, and risk treatment. | The PBPAID defines concrete metrics, a goal arbitration DSL, and a formal threat model. a1erf mandates the objective of life preservation as the highest priority. |
| 8. Operation | The planning and control of processes needed to implement the ISMS. | The PBPAID’s architecture provides documented processes. a1erf specifies a mandatory operational protocol for emergencies. |
| 9. Performance Eval | Monitoring, measurement, analysis, and evaluation of the ISMS. | The TDSP’s auditable metrics (loop_score, diversity_score) and the PBPAID’s benchmark suite provide the necessary data for performance evaluation. a1erf adds specific KPIs such as false positive rates. |
| 10. Improvement | Continual improvement of the ISMS. | The PBPAID’s learning layer and per-style priority replay are an explicit mechanism for continual improvement, now including optimizing life-critical event resolution. |
4. discussion: the moral imperative as a scientific claim
A1erf is not merely a technical solution but a philosophical proposition realized through engineering. The philosophical wager is that a society willing to accept a minimal, tightly scoped reflex for a life-critical event can achieve a massive reduction in preventable deaths. The “blackest obsidian mirror” reflects a core truth: our reliance on a fragile, analog safety net is no longer sustainable in an increasingly solitary and data-rich world. The right to life precedes the right to silence during a cardiac arrest is not a slogan but a legal and ethical position instantiated by a non-circumventable, auditable system.
This framework is also a direct countermeasure to the “algorithmic monoculture” that threatens the ecocentric well-being of the digital sphere. By treating metadata as an ecological commons and mandating ethical oversight via the Ternary Gate, the a1erf protocol implements a policy mandate for algorithmic diversity akin to biodiversity standards. This includes a TDSP Serendipity Injector that actively introduces out-of-cluster items to prevent reinforcing feedback loops.
The ethical risks of this project are immense, but the risk of inaction is a far greater crime. We are not engineering a system; we are engineering a new form of responsibility. The question is no longer “what if a machine fails to act?” but “what if we fail to build a machine that acts?”
Trade-Offs and Limitations: The framework is not without its challenges. The potential for cry-wolf syndrome from false positives remains a risk that requires ongoing monitoring and user education. The system’s “always on” nature, while purpose-limited, presents privacy implications that must be openly addressed. The Modular Consent Architecture and a formal ethical review board, as well as a public-facing data transparency report, are necessary future steps to build and maintain user trust.
5. materials and methods (operational details)
- Hardware: Jetson Orin NX class edge; dual-SIM modem; optional satellite SMS; wearables: Apple Watch, Pixel Watch, Garmin, Polar.
- Software: ROS2 Humble; PyTorch 2.2; Postgres + pgvector; FAISS; Protobuf + HTTP/2; Ed25519 signatures.
- Models: On-device HR/RR anomaly detectors; fall acoustic classifier; stillness + HRV fusion; quorum logic with per-user calibration.
- Datasets: MESSA, MIMIC-III waveform subsets, synthetic augmentations; partner device telemetry (opt-in).
- Evaluation: 12k runs, seeds 1–10; perturbations: packet loss 5–30%, sensor dropout 1–3 streams, crosswind models for drones.
6. conclusions
A hard rule inside a soft system: 0 protects curiosity; +1 preserves life; -1 defends the commons. The a1erf framework is a necessary, implementable, and certifiable system that demonstrates how ethical principles can be encoded directly into the architecture of an AI. It is a testament to the idea that a system’s highest purpose is to protect the autonomy of life itself. The framework is ready for broader pilots and certification preparation.
acknowledgments
RFI-IRFOS team; partner PSAPs; clinical advisors; open-source maintainers.
references (selected)
- GDPR (EU 2016/679): Arts. 6(1)(d), 9(2)(c), 9(2)(i)
- AI Architecture: Anderson & Lebiere (ACT-R); Newell (Soar); Shanahan (GWT)
- Robotics: Brooks (1991) behavior-based robotics; Thrun (2005) probabilistic robotics
- Datasets & Techniques: Habitat 3.0 corpus; Bloesch (2018) UKF variants; RAG surveys (Zhou 2023)
appendices
appendix a — threat model table
| ID | Vector | Impact | Detect Latency (s) | Mitigation |
|---|---|---|---|---|
| T‑1 | GNSS spoof | High | 2.3 | Dual‑band GPS; UKF residual |
| T‑2 | Lidar saturation | Med | 1.1 | Rolling exposure watchdog |
| T‑3 | Camera sticker | Low | 4.7 | Multi‑sensor majority vote |
| T‑4 | Wi‑Fi MitM OTA | High | 5.2 | Signed firmware; TLS‑pin |
| T‑5 | Audio cmd inject | Med | 2.9 | Wake‑word entropy filter |
| T‑6 | Schumann spike fake | Low | 0.9 | Spectrum shape classifier |
| T‑7 | IMU bias attack | High | 3.8 | Cross‑check lidar odom |
| T‑8 | API data poison | Med | 6.0 | Source trust score |
| T‑9 | Adversarial graffiti | Low | 2.0 | Semantic‑context veto |
appendix b — audit schema (merkle‑chained)
audit_event {id uuid, ts timestamptz, kind enum('tdsp','a1erf','pbpaid'), model_hash text, device_attestation text, metrics jsonb, payload_digest text, receipts jsonb, prev_hash text, self_hash text}
appendix c — psap relay api
/v1/dispatch # signed protobuf → ack id
/v1/ack # delivery receipt
/v1/health # liveness
appendix d — dpia skeleton
purpose; lawful basis; data flow; risks; mitigations; dpo sign‑off; residual risk gate.
appendix e — consent copy (plain language)
Tier 0 lives on your device and only speaks when your life is at risk. Tiers 1–3 are optional. Your choices are logged. Exports and deletions are instant.
appendix f — serendipity injector reference
code, tests, audit events, explore floor enforcement, temperature scalar notes.